Differential privacy is a property that ensures that private data of an agent is protected, while allowing for statistical inferences from aggregates of the data [1]}.
This makes it unlikely that an adversary will learn anything meaningful about sensitive data.
Attractive features of differential privacy include compositionality, resilience to post-processing, and robustness to side information.
The notion of differential privacy is mathematically defined using a notion of neighboring data points to characterize data points that are sufficiently close to each other according to some metric/ norm.
Let \(d, d^{\prime } \in \mathcal {D}\)  be neighboring inputs.
